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Improved Recurrent Neural Network Schema for Validating Digital Signatures in VANET

Author

Listed:
  • Arpit Jain

    (Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation (KLEF), Vaddeswaram 522302, Andhra Pradesh, India)

  • Jaspreet Singh

    (Department of Computer Science and Engineering, Chandigarh University, Gharuan, Mohali 140413, Punjab, India
    These authors contributed equally to this work.)

  • Sandeep Kumar

    (Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation (KLEF), Vaddeswaram 522302, Andhra Pradesh, India
    These authors contributed equally to this work.)

  • Țurcanu Florin-Emilian

    (Department of Building Services, Faculty of Civil Engineering and Building Services, Gheorghe Asachi Technical University of Iasi, 700050 Jassy, Romania)

  • Mihaltan Traian Candin

    (Faculty of Building Services Cluj-Napoca, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania)

  • Premkumar Chithaluru

    (Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation (KLEF), Vaddeswaram 522302, Andhra Pradesh, India
    These authors contributed equally to this work.)

Abstract

Vehicular ad hoc networks (VANETs) allow communication between stationary or moving vehicles with the assistance of wireless technology. Among various existing issues in smart VANETs, secure communication is the key challenge in VANETs with a 5G network. Smart vehicles must communicate with a broad range of advanced road systems including traffic control and smart payment systems. Many security mechanisms are used in VANETs to ensure safe transmission; one such mechanism is cryptographic digital signatures based on public key infrastructure (PKI). In this mechanism, secret private keys are used for digital signatures to validate the identity of the message along with the sender. However, the validation of the digital signatures in fast-moving vehicles is extremely difficult. Based on an improved perceptron model of an artificial neural network (ANN), this paper proposes an efficient technique for digital signature verification. Still, manual signatures are extensively used for authentication across the world. However, manual signatures are still not employed for security in automotive and mobile networks. The process of converting manual signatures to pseudo-digital-signatures was simulated using the improved Elman backpropagation (I-EBP) model. A digital signature was employed during network connection to authenticate the legitimacy of the sender’s communications. Because it contained information about the vehicle on the road, there was scope for improvement in protecting the data from attackers. Compared to existing schemes, the proposed technique achieved significant gains in computational overhead, aggregate verification delay, and aggregate signature size.

Suggested Citation

  • Arpit Jain & Jaspreet Singh & Sandeep Kumar & Țurcanu Florin-Emilian & Mihaltan Traian Candin & Premkumar Chithaluru, 2022. "Improved Recurrent Neural Network Schema for Validating Digital Signatures in VANET," Mathematics, MDPI, vol. 10(20), pages 1-23, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:20:p:3895-:d:948094
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    References listed on IDEAS

    as
    1. Eiman ElGhanam & Ibtihal Ahmed & Mohamed Hassan & Ahmed Osman, 2021. "Authentication and Billing for Dynamic Wireless EV Charging in an Internet of Electric Vehicles," Future Internet, MDPI, vol. 13(10), pages 1-19, October.
    2. Irshad Ahmed Abbasi & Adnan Shahid Khan, 2018. "A Review of Vehicle to Vehicle Communication Protocols for VANETs in the Urban Environment," Future Internet, MDPI, vol. 10(2), pages 1-15, January.
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